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FBI UCR — State Crime Statistics vs National

fbi.crime.state_offenses
Read-onlyIdempotent

Retrieve monthly state-level UCR crime statistics from the FBI Crime Data Explorer, including a side-by-side national comparison. Returns offense counts, rates per 100,000 people, and clearance counts for both the requested state and the national average. Supports all 50 US states and DC (use 2-letter abbreviation, e.g. CA, TX, NY). Same 10 offense types as the national endpoint. Ideal for comparing a state's crime rate against the national benchmark. Source: FBI UCR / CDE, US Gov public domain, no auth required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYesTwo-letter US state abbreviation (e.g. CA, TX, NY, FL). All 50 states + DC supported.
to_monthNoEnd month in MM-YYYY format (e.g. 12-2022). Defaults to last full year.
from_monthNoStart month in MM-YYYY format (e.g. 01-2019). Data available from 01-1979.
offense_typeYesFBI UCR offense category. violent-crime (murder+rape+robbery+assault), property-crime (burglary+larceny+auto+arson), or individual offense type

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark this read-only, idempotent, and non-destructive, so the description correctly describes a retrieve operation without contradiction. It adds useful behavioral context: exact returned measures (counts, rates per 100,000, clearances), state and national comparison, 50 states + DC coverage, public-domain source, and no auth required.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core action, followed by return details, scope, comparison use case, and provenance. Every sentence adds value without redundant filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Between the description, schema, output schema, and annotations, an agent has everything needed to call the tool correctly: required parameters, optional date range, state format, offense enums, safety profile, and data source. No critical behavioral or invocation detail is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and each parameter already has descriptive docs, so the description adds limited new meaning. It reinforces state abbreviation format and offense-type consistency with the national endpoint, but mostly repeats schema content.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Retrieve') and names the exact resource: monthly state-level UCR crime statistics from the FBI Crime Data Explorer. It clearly distinguishes this from sibling fbi.crime tools by emphasizing state-level coverage plus a national side-by-side comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states the intended use case ('Ideal for comparing a state's crime rate against the national benchmark') and clarifies monthly granularity, which helps separate it from state_annual and national_offenses. It does not explicitly list exclusions or name sibling alternatives, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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